Big Data, HPC, Content Storage

Getting a Handle on Big Data

Data sets that are large and complex need special attention. Daystrom can help your organization analyze, organize, and streamline big and complex data using big data analytics.

This process examines large amounts of differing types of data to uncover hidden patterns, reveal unknown correlations, and expose data idiosyncrasies. These findings can provide competitive advantages over rival organizations, resulting in concrete business benefits such as more effective marketing and increased revenue.

Daystrom also has expertise in designing content distribution architecture , which allows end users access to valuable sub-elements of the data set, such as a movie. Daystrom's leading-edge architecture provides access to large volumes of data, solves complex problems , and enhances performance .

Our experts can also discuss whether your organization might benefit from parallel (cluster) file systems, which expand storage bandwidth and capacity almost linearly across multiple modular storage nodes. With a long track record of success, they provide optimum performance, are high scalable, and are cost-effective. They are used mainly for applications that create large amounts of unstructured data. Despite their advanced nature, cluster file systems often lack features that are commonplace in general IT. Daystrom can assist you in closing this "features gap" so that you can comfortably increase the use of these systems within your enterprise.

Regardless of the chosen technology, Daystrom experts can work with your team to provide storage solutions that are scalable, tiered, and self-managing.

What is big data?

Many different types of data sets are considered big data—scientific research data from logs and sensors, financial transactions, static and moving images, and unstructured text posted across the Internet. The information may be simple or complex, moving in real time or in short bursts, but it is "big" when measured in aggregate.

The Analytics Edge

Top-performing companies use data analytics five times more than lower-performing organizations.